可再生科学和深度软件可变性

M. Acher
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引用次数: 2

摘要

生物学、医学、物理学、天体物理学、化学:所有这些科学领域都需要用越来越复杂的软件系统来处理大量的数据。为了实现可复制的科学,前面有几个挑战,涉及多学科合作和以软件为中心的社会技术创新。尽管数据和代码的可用性,一些研究报告说,用不同的软件分析相同的数据可能会导致不同的结果。我认为这个问题是软件深度可变性的表现:许多因素(操作系统、第三方库、版本、工作负载、编译时选项和标志等)本身都受到可变性的影响,可以改变结果,甚至可以戏剧性地改变一些科学研究的结论。在这个主题演讲中,我认为软件的深度可变性对可复制科学来说是一种威胁,也是一个机会。我首先概述了一些关于(深度)软件可变性的工作,报告了可变性层之间复杂交互的初步证据。然后,我将正在进行的变异性建模和深度软件变异性方面的工作联系起来,以寻求可复制的科学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reproducible Science and Deep Software Variability
Biology, medicine, physics, astrophysics, chemistry: all these scientific domains need to process large amount of data with more and more complex software systems. For achieving reproducible science, there are several challenges ahead involving multi-disciplinary collaboration and socio-technical innovation with software at the center of the problem. Despite the availability of data and code, several studies report that the same data analyzed with different software can lead to different results. I am seeing this problem as a manifestation of deep software variability: many factors (operating system, third-party libraries, versions, workloads, compile-time options and flags, etc.) themselves subject to variability can alter the results, up to the point it can dramatically change the conclusions of some scientific studies. In this keynote, I argue that deep software variability is a threat and also an opportunity for reproducible science. I first outline some works about (deep) software variability, reporting on preliminary evidence of complex interactions between variability layers. I then link the ongoing works on variability modelling and deep software variability in the quest for reproducible science.
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